Humans visually perceive the world with passive RGB views and are able to perform sophisticated interactions with objects even if the objects are unseen previously, translucent, reflective, or transparent.
Robotic grasping mostly relies on RGB cameras [1]}, or active depth sensors such as ToF [2]}, LiDAR [3]}, and active stereo [4]}, to perform arbitrary object grasping using the simplified grasping representation in \(SE(2)\)  or \(SE(3)\)  with no prior knowledge such as explicit object models or category information.
Existing learning–based approaches can be categorized in two directions, namely planar grasping and 6-DoF grasping.
Planar grasping [5]}, [6]}, [7]}, [8]} relies on a simple but effective grasping representation, which defines grasps as oriented bounding boxes.
Although such a low DoF grasp representation reduces the task to a simple detection problem, it limits the performance in 3D manipulation tasks. 6-DoF grasping enjoys more dexterity than planar grasping, which is suitable for handling complex scenarios. Most 6-DoF grasping methods [9]}, [10]}, [11]}, [12]}, [13]} extract geometric information from dense input and use it to score the grasp candidates. Despite the satisfactory grasping performance on common objects even if the shape is unknown, the reliance on the dense 3D information makes them vulnerable when the input sensing is unstable, especially on
transparent or highly reflective surfaces [14]}. Cross-modality fusion approaches for perception tasks can be used to combine RGB images with other sensor inputs [15]}, [16]}, [17]}.
This allows to deal with the aforementioned limitation and shows promising results [18]}, [19]}.
Nevertheless, these methods still rely on external depth information as indispensable input, which makes the methods less convienient and more expensive.
Motivated by this, we aim to fully explore the ability of the easily accessible and stable RGB modality on the 6-DoF robotic grasping task without any depth input.
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